LFM3-1.7B vs Step 5 Preview

Liquid AI · US | StepFun · China · Updated June 2026

Quick verdict

Pick LFM3-1.7B for very small open-weight text model - about 1.73b parameters with a 256k-token context for on-device use or liquid says it can power agentic applications that used to need 9b parameters. Pick Step 5 Preview for sparse moe architecture — 600b total parameters, only 27b active per token — built for long-horizon agentic work across large codebases and documents or 1m-token context window (64k max output) at $1/$2.70 per million tokens, among the cheapest frontier-scale context windows available. On a tight budget at scale, LFM3-1.7B is the value pick.

LFM3-1.7B (Liquid AI, US) and Step 5 Preview (StepFun, China) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. LFM3-1.7B is liquid AI's October 27, 2026 on-device text model - about 1.73B parameters and 256K context, with open weights under a license capped at $10M annual revenue. Step 5 Preview is stepFun's September 20, 2026 flagship preview — a 600B-parameter sparse MoE model (27B active) with a 1M-token context window at $1/$2.70 per million tokens, with open weights published October 15. They diverge most on price and context window — each quantified below from the models' real specs.

Key differences at a glance

▸Context window: Step 5 Preview holds 3.9× more — 1M tokens (~1,500 pages) vs 256K tokens (~384 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
▸Recency: LFM3-1.7B is the newer model by about 37 days (released October 27, 2026), usually meaning fresher training data and capabilities.
▸Ecosystem: this is a US-vs-China matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.

Side-by-side specs

Spec	LFM3-1.7B	Step 5 Preview
Provider	Liquid AI (US)	StepFun (China)
Released	October 27, 2026	September 20, 2026
Context window	256K tokens (~384 pages)	1M tokens (~1,500 pages)
Price (in/out)	Open weight (self-host / free)	$1/$2.7 per 1M tokens
Open weight?	Yes — self-hostable	Yes — self-hostable
Modalities	text	text, image, code
SWE-Bench Verified	Not published	Not published
MRCR v2 @ 1M	Not published	Not published

Who wins what

Very small open-weight text model - about 1.73B parameters with a 256K-token context for on-device use

LFM3-1.7B

Step 5 Preview is comparatively weak here — trails closed frontier models on coding — 67.7% on DeepSWE v1.1 vs GPT-6 Astra's 74.1% and Claude Opus 5's 74.0%

Liquid says it can power agentic applications that used to need 9B parameters

LFM3-1.7B

Liquid AI's October 27, 2026 on-device text model - about 1.73B parameters and 256K context, with open weights under a license capped at $10M annual revenue — and it is the newer of the two.

Part of a six-model LFM3 family covering text, vision, audio, omni and decision tasks

LFM3-1.7B

LFM3-1.7B lists part of a six-model LFM3 family covering text, vision, audio, omni and decision tasks among its strengths; Step 5 Preview does not.

Sparse MoE architecture — 600B total parameters, only 27B active per token — built for long-horizon agentic work across large codebases and documents

Step 5 Preview

Its 1M tokens window holds about 3.9× more than LFM3-1.7B's 256K tokens in a single prompt.

1M-token context window (64K max output) at $1/$2.70 per million tokens, among the cheapest frontier-scale context windows available

Step 5 Preview

LFM3-1.7B is comparatively weak here — no published per-token API price found

67.7% on DeepSWE v1.1, ahead of open-weight rivals Kimi K3 (67.5%) and GLM-5.3 (66.9%)

Step 5 Preview

Lowest cost at scale

LFM3-1.7B

Its weights are open, so at volume you pay for your own hardware instead of Step 5 Preview's $1/$2.7 per 1M tokens.

Largest single-prompt input

Step 5 Preview

Its 1M tokens window is about 3.9× larger than LFM3-1.7B's 256K tokens, fitting roughly 1,500 pages in one prompt.

Which should you pick?

A cost-sensitive startup shipping high volume

→ LFM3-1.7B

At Open weight (self-host / free) it undercuts Step 5 Preview, and on millions of tokens that margin decides the monthly bill.

Someone analysing very long documents or codebases

→ Step 5 Preview

Larger 1M tokens window fits more in one prompt.

Anyone whose priority is very small open-weight text model - about 1.73b parameters with a 256k-token context for on-device use

→ LFM3-1.7B

It is specifically built for that.

Anyone whose priority is sparse moe architecture — 600b total parameters, only 27b active per token — built for long-horizon agentic work across large codebases and documents

→ Step 5 Preview

That is its strongest area.

An enterprise with regional data-residency rules

→ LFM3-1.7B or Step 5 Preview

Origin (US vs China) affects where data is processed and which compliance regime applies — check the provider's terms for your region.

LFM3-1.7B: where it fits

Liquid AI's October 27, 2026 on-device text model - about 1.73B parameters and 256K context, with open weights under a license capped at $10M annual revenue. Released October 27, 2026 by Liquid AI, it is built for very small open-weight text model - about 1.73B parameters with a 256K-token context for on-device use, liquid says it can power agentic applications that used to need 9B parameters, part of a six-model LFM3 family covering text, vision, audio, omni and decision tasks, and supports 16 languages.

Its trade-offs are real: lFM Open License v1.0 covers commercial use only below $10M annual revenue; larger firms need a paid license, liquid says it is not recommended for agentic coding or complex multi-step reasoning because of its size, text-only; vision and audio need other LFM3 checkpoints, and no published per-token API price found. As an open-weight model, its running cost is your own hardware rather than a per-token fee.

Step 5 Preview: where it fits

StepFun's September 20, 2026 flagship preview — a 600B-parameter sparse MoE model (27B active) with a 1M-token context window at $1/$2.70 per million tokens, with open weights published October 15. Released September 20, 2026 by StepFun, it is built for sparse MoE architecture — 600B total parameters, only 27B active per token — built for long-horizon agentic work across large codebases and documents, 1M-token context window (64K max output) at $1/$2.70 per million tokens, among the cheapest frontier-scale context windows available, 67.7% on DeepSWE v1.1, ahead of open-weight rivals Kimi K3 (67.5%) and GLM-5.3 (66.9%), and scores 44 on the Artificial Analysis Intelligence Index, ranking 27th of 653 models tracked.

Its trade-offs: trails closed frontier models on coding — 67.7% on DeepSWE v1.1 vs GPT-6 Astra's 74.1% and Claude Opus 5's 74.0%, weights are public under the StepFun Model License 1.0, which is Apache-2.0-style but adds attribution and commercial conditions, a 'Preview' release, not StepFun's finished flagship — positioning and pricing may change, and stepCodeBench and DeepSWE figures are StepFun's own reported numbers, not yet independently reproduced at scale. At $1 in / $2.7 out per million tokens, it sits in the budget price band.

The bottom line for this matchup

This is less "which is smarter" and more "which ecosystem fits." LFM3-1.7B (US) and Step 5 Preview (China) differ on pricing philosophy, data-residency, and tooling as much as on raw scores. LFM3-1.7B is the cheaper option, which matters at volume. The pragmatic move is to run one real task through both and judge the outputs against your own constraints — including where your data is allowed to be processed.

Want both LFM3-1.7B and Step 5 Preview without two subscriptions? LumiChats gives you these plus 40+ models under one ₹69/day pass (about $1/day) — draft with one, cross-check with the other.

See pricing

Frequently asked questions

Is LFM3-1.7B or Step 5 Preview better for coding?

Public SWE-Bench figures are not available for either model, so the honest test is your own repository — run an identical real bug through both. By design, LFM3-1.7B leans toward very small open-weight text model - about 1.73b parameters with a 256k-token context for on-device use while Step 5 Preview leans toward sparse moe architecture — 600b total parameters, only 27b active per token — built for long-horizon agentic work across large codebases and documents, and that positioning usually predicts which feels better on your codebase.

Which is cheaper, LFM3-1.7B or Step 5 Preview?

LFM3-1.7B is cheaper — Open weight (self-host / free) vs $1/$2.7 per 1M tokens.

Which has the bigger context window?

Step 5 Preview — 1M tokens vs 256K tokens, about 3.9× larger. Useful only if the model actually reasons over the full window, which not all do.

Can I use both LFM3-1.7B and Step 5 Preview together?

Yes — a multi-model platform like LumiChats gives you LFM3-1.7B, Step 5 Preview and 40+ others under one ₹69/day pass (about $1/day), so you can draft with one and cross-check with the other instead of buying two subscriptions.

Which is newer, LFM3-1.7B or Step 5 Preview?

LFM3-1.7B — released October 27, 2026, about 37 days after Step 5 Preview.

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Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.